迭代器与函数Python学习（四）

1.1 迭代器：

迭代的工具

1.1.1 什么是迭代：

指的是一个重复的过程，每一次重复称为一次迭代，并且每一次重复的结果是下一次重复的初始值

while True:

    print('=====>')

l=['a','b','c']

count=0

while count < len(l):

    print(l[count])

    count+=1

1.1.2 为什么要有迭代器？

对于序列类型：str，list，tuple，可以依赖索引来迭代取值，

但是对于dict，set，文件,python必须为我们提供一种不依赖于索引的迭代取值的方式-》迭代器

1.1.3 可迭代的对象(下列都是)：obj.__iter__

name='egon'

l=[1,2,3]

t=(1,2,3)

d={'name':'egon','age':18,'sex':'male'}

s={'a','b','c'}

f=open('a.txt','w',encoding='utf-8')

name.__iter__

l.__iter__

t.__iter__

d.__iter__

s.__iter__

f.__iter__

1.1.4 迭代器对象（文件是）：obj.__iter__,obj.__next__

f.__iter__

f.__next__

1.1.5 总结：

1 可迭代对象不一定是迭代器对象

2 迭代器对象一定是可迭代的对象

3 调用obj.__iter__()方法,得到的是迭代器对象(对于迭代器对象，执行__iter__得到的仍然是它本身)

d={'name':'egon','age':18,'sex':'male'}

d_iter=d.__iter__()

 

f=open('a.txt','w',encoding='utf-8')

f_iter=f.__iter__().__iter__().__iter__().__iter__()

 

print(f_iter is f)

d={'name':'egon','age':18,'sex':'male'}

d_iter=d.__iter__()

 

print(d_iter.__next__())

print(d_iter.__next__())

print(d_iter.__next__())

print(d_iter.__next__())

迭代器d_iter没有值了，就会抛出异常StopIteration

f=open('a.txt','r',encoding='utf-8')

print(f.__next__())

print(f.__next__())

print(f.__next__())

print(f.__next__())

f.close()

l=['a','b','c']

l_iter=l.__iter__()

 

print(l_iter.__next__())

print(l_iter.__next__())

print(l_iter.__next__())

print(l_iter.__next__())

d={'name':'egon','age':18,'sex':'male'}

d_iter=iter(d) #d_iter=d.__iter__() 

len(obj) 等同于obj.__len__()

1.1.6 for循环

while True:

    try:

        print(next(d_iter)) #print(d_iter.__next__())

    except StopIteration:

        break

 

print('=>>>')

print('=>>>')

print('=>>>')

print('=>>>')

for循环详解:

1、调用in后的obj_iter=obj.__iter__()

2、k=obj_iter.__next__()

3、捕捉StopIteration异常，结束迭代

d={'name':'egon','age':18,'sex':'male'}

for k in d:

    print(k)

1.1.7 总结迭代器的优缺点：

1.1.7.1  优点:

1、提供一种统一的、不依赖于索引的取值方式，为for循环的实现提供了依据

2、迭代器同一时间在内存中只有一个值——》更节省内存，

1.1.7.2  缺点：

1、只能往后取，并且是一次性的

2、不能统计值的个数，即长度

l=[1,2,3,4,5,6]

l[0]

l[1]

l[2]

l[0]

 
l_iter=l.__iter__()

# print(l_iter)

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

l_iter=l.__iter__()

print(next(l_iter))

print(next(l_iter))

print(next(l_iter))

 

print(len(l_iter))

1.2 生成器

1.2.1 什么是生成器：

只要在函数体内出现yield关键字，那么再执行函数就不会执行函数代码，会得到一个结果，该结果就是生成器

def func():

    print('=====>1')

    yield 1

    print('=====>2')

    yield 2

    print('=====>3')

    yield 3

生成器就是迭代器
g=func()

 

res1=next(g)

print(res1)

 

 

res2=next(g)

print(res2)

 

 

res3=next(g)

# print(res3)

1.2.2 yield的功能：

1、yield为我们提供了一种自定义迭代器对象的方法

2、yield与return的区别1：yield可以返回多次值 #2：函数暂停与再继续的状态是由yield帮我们保存的 

obj=range(1,1000000000000000000000000000000000000000000000000000000000000000,2)

obj_iter=obj.__iter__()

print(next(obj_iter))

print(next(obj_iter))

print(next(obj_iter))

print(next(obj_iter))

print(next(obj_iter)) 

def my_range(start,stop,step=1):

    while start < stop:

        yield start #start=1

        start+=step #start=3

 
g=my_range(1,5,2)

print(g)

 

print(next(g))

print(next(g))

print(next(g))

print(next(g))

print(next(g))

print(next(g))

print(next(g))

for i in my_range(1,5,2):

    print(i)

1.2.3 小练习：:tail -f access.log | grep '404'

import time

def tail(filepath):

    with open(filepath,'rb') as f:

        f.seek(0,2)

        while True:

            line=f.readline()

            if line:

                yield line

            else:

                time.sleep(0.05)

 

def grep(lines,pattern):

    for line in lines:

        line=line.decode('utf-8')

        if pattern in line:

            yield line

 

 

lines=grep(tail('access.log'),'404')

 

for line in lines:

    print(line)

1.2.4 yield表达式形式的用法（了解知识点）

def eater(name):

    print('%s ready to eat' %name)

    food_list=[]

    while True:

        food=yield food_list#food=yield='一盆骨头'

        food_list.append(food)

        print('%s start to eat %s' %(name,food))

 

 

e=eater('alex')

#首先初始化：

print(e.send(None)) # next(e)

#然后e.send:1 从暂停的位置将值传给yield  2、与next一样

print(e.send('一桶泔水'))

print(e.send('一盆骨头'))

1.3 追加文件

with open('access.log','a',encoding='utf-8') as f:

    f.write('bbbbb 404
')

    f.flush()

1.4 面向过程编程

grep -rl 'python' /etc

补充：os.walk

import os

g=os.walk(r'D:videopython20期day4a')

# print(next(g))

# print(next(g))

# print(next(g))

# print(next(g))

for pardir,_,files in g:

    for file in files:

        abs_path=r'%s\%s' %(pardir,file)

        print(abs_path)

1.4.1 分析一：

1.4.1.1  第一步：拿到一个文件夹下所有的文件的绝对路径

import os

 

def search(target): #r'D:videopython20期day4a'

    while True:

        filepath=yield #fllepath=yield=r'D:videopython20期day4a'

        g=os.walk(filepath)

        for pardir, _, files in g:

            for file in files:

                abs_path = r'%s\%s' % (pardir, file)

                # print(abs_path)

                target.send(abs_path)

 

search(r'D:videopython20期day4a')

search(r'D:videopython20期day4')

1.4.1.2  第二步：打开文件拿到文件对象f

def opener():

    while True:

        abs_path=yield

        print('opener func--->',abs_path)

 

 

target=opener()

next(target) #target.send('xxxx')

 

g=search(target)

next(g)

g.send(r'D:videopython20期day4a')

1.4.2 分析二：

1.4.2.1  第一步：拿到一个文件夹下所有的文件的绝对路径

import os

def init(func):

    def inner(*args,**kwargs):

        g=func(*args,**kwargs)

        next(g)

        return g

    return inner

 

@init

def search(target):  # r'D:videopython20期day4a'

    while True:

        filepath = yield

        g = os.walk(filepath)

        for pardir, _, files in g:

            for file in files:

                abs_path = r'%s\%s' % (pardir, file)

                #把abs_path传给下一个阶段

                target.send(abs_path)

1.4.2.2  第二步：打开文件拿到文件对象f

@init

def opener(target):

    while True:

        abs_path = yield

        with open(abs_path,'rb') as f:

            #把(abs_path,f)传给下一个阶段

            target.send((abs_path,f))

 

1.4.2.3  第三步：读取f的每一行内容

@init

def cat(target):

    while True:

        abs_path,f=yield

        for line in f:

            #把(abs_path,line)传给下一个阶段

            res=target.send((abs_path,line))

            #满足某种条件，break掉for循环

            if res:

                break

 

1.4.2.4  第四步：判断'python' in line

@init

def grep(target,pattern):

    pattern = pattern.encode('utf-8')

    res=False

    while True:

        abs_path,line=yield res

        res=False

        if pattern in line:

            #把abs_path传给下一个阶段

            res=True

            target.send(abs_path)

1.4.2.5  第五步：打印文件路径

@init

def printer():

    while True:

        abs_path=yield

        print('<%s>' %abs_path)

 

g=search(opener(cat(grep(printer(),'python')))) #'python' in b'xxxxx'

g.send(r'D:videopython20期day4a')

面向过程编程：核心是过程二字，过程指的就是解决问题的步骤，即先干什么后干什么，基于该思路编写程序就好比设计一条流水线，是一种机械式的思维方式

1.4.3 面向过程编程优缺点

优点：

复杂的问题流程化、进而简单化

缺点：

可扩展性差

1.5 三元表达式

def my_max(x,y):

    if x >= y:

        return x

    else:

        return y

 

x=10

y=20

 

# res=x if x >= y else y

# print(res)

 

name=input('>>: ').strip()

 

res='Sb' if name == 'alex' else 'NB'

print(res)

 

1.6 列表推导式与生成器表达式

1.6.1 列表推导式

l=[]

for i in range(1,11):

    res='egg'+str(i)

    l.append(res)

 

print(l)

 

l=['egg'+str(i) for i in range(1,11)]

print(l)

 

l1=['egg'+str(i) for i in range(1,11) if i >= 6]

print(l1)

 

l1=[]

for i in range(1,11):

    if i >= 6:

        l1.append('egg'+str(i))

1.6.2 生成器表达式

g=('egg'+str(i) for i in range(0,1000000000000000000000000000000000))

print(g)

print(next(g))

print(next(g))

print(next(g))

1.6.3 练习

names=['egon','alex_sb','wupeiqi','yuanhao']

 

names=[name.upper() for name in names]

print(names)

 

sbs=[name for name in names if name.endswith('sb')]

print(sbs)

 

 

obj=list('abcdef')

print(obj)

 

print(max([1,2,3,4,5]))

 

g=(i for i in range(10))

print(max(g))

 

print(max(g))

 

with open('a.txt','r',encoding='utf-8') as f:

    l=[]

    for line in f:

        # print(len(line))

        l.append(len(line))

 

    g=(len(line) for line in f)

    res=max(g)

    print(res)

 

    print(max(len(line) for line in f))

 

    print(sum(len(line) for line in f))

 

1.7 递归调用：

在调用一个函数的过程中，直接或者间接又调用该函数本身，称之为递归调用

1.7.1 递归必备的两个阶段：

1、递推

2、回溯

import sys

print(sys.getrecursionlimit())

sys.setrecursionlimit(2000)

print(sys.getrecursionlimit())

 

def func(n):

    print('---->',n)

    func(n+1)

 

func(0)

 

 

def bar():

    print('from bar')

    func()

 

def func():

    print('from func')

    bar()

 

func()

 
age(5) = age(4) + 2

age(4) = age(3) + 2

age(3) = age(2) + 2

age(2) = age(1) + 2

 

age(1) = 18

 

age(n)=age(n-1)+2 # n > 1

age(1) = 18 #n = 1

 
 
def age(n):

    if n == 1:

        return 18

    return age(n-1) + 2

 

res=age(5)

print(res)

 

 

l=[1,[2,[3,[4,[5,[6,[7,]]]]]]]

 

 

def func(l):

    for item in l:

        if type(item) is list:

            func(item)

        else:

            print(item)

 

 

 

def func():

    print('===>')

    func()

 

func()

 

1.8 二分法（了解的知识点）

l=[1,2,10,30,33,99,101,200,301,402] #从小到大排列的数字列表

 

def binary_search(l,num):

    print(l)

    if len(l) == 0:

        print('not exists')

        return

    mid_index=len(l) // 2

    if num > l[mid_index]:

        #往右找

        binary_search(l[mid_index+1:],num)

 

    elif num < l[mid_index]:

        #往左找

        binary_search(l[0:mid_index],num)

    else:

        print('find it')

 

# binary_search(l,301)

binary_search(l,302)

1.9 匿名函数

def func(): #func=内存地址

    print('from func')

 

func()

func()

 

def my_sum(x,y):

    return x+y

print(lambda x,y:x+y)

print((lambda x,y:x+y)(1,2))

 

func=lambda x,y:x+y

# print(func)

print(func(1,2))

max，min，sorted,map,reduce,filter

salaries={

    'egon':3000,

    'alex':100000000,

    'wupeiqi':10000,

    'yuanhao':2000

}

print(max(salaries))

s='hello'

l=[1,2,3]

g=zip(s,l)

# print(g)

print(list(g))

g=zip(salaries.values(),salaries.keys())

# print(list(g))

print(max(g))

 
def func(k):

    return salaries[k]

 

print(max(salaries,key=func)) #key=func('egon')

 

print(max(salaries,key=lambda k:salaries[k])) #key=func('egon')

print(min(salaries,key=lambda k:salaries[k])) #key=func('egon')

  

sorted

salaries={

    'egon':3000,

    'alex':100000000,

    'wupeiqi':10000,

    'yuanhao':2000

}

print(sorted(salaries,key=lambda k:salaries[k]))

print(sorted(salaries,key=lambda k:salaries[k],reverse=True))

 

map,reduce,filter

names=['alex','wupeiqi','yuanhao']

l=[]

for name in names:

    res='%s_SB' %name

    l.append(res)

 

print(l)

 

g=map(lambda name:'%s_SB' %name,names)

# print(g)

print(list(g))

names=['alex_sb','wupeiqi_sb','yuanhao_sb','egon']

g=filter(lambda x:x.endswith('sb'),names)

print(g)

print(list(g))

from functools import reduce

print(reduce(lambda x,y:x+y,range(1,101),100))

1.10 内置函数（了解）

print(abs(-1))

print(all([1,'a','b',0]))

print(all([]))

print(any([None,False,0,1]))

print(any([]))

print(bin(11))

print(hex(11))

print(oct(11))

print('xxx'.encode('utf-8'))

print(bytes('xxx',encoding='utf-8'))

print(callable(max))

print(chr(65))

# print(chr(90))

# print(chr(39))

print(ord('A'))

print(ord('@'))

import os

print(dir(os))

 
s=set({1,2,3})

s.add(4)

print(s)

s=frozenset({1,2,3}) #不可变集合

 

print(hash('xxx'))

l=[1,2,'a',4]

print(list(reversed(l)))

s=slice(1,5,2)

l=['a','b','c','d','e']

# print(l[1:5:2])

# print(l[1:5:2])

 

print(l[s])

print(vars() is locals())

obj.__dict__() #vars(obj)

1.11 面向对象

classmethod

staticmethod

property

hasattr

getattr

setattr

delattr

isinstance

issubclass

object

super

import

__import__

choice=input('>>: ')

print(choice,type(choice))

# import 'time'
m=__import__(choice)

m.sleep(10) 

1.12 掌握：

1.12.1 divmod

# print(divmod(10011,25))

1.12.2 enumerate

l=['a','b','c']

 

for i in l:

    print(l.index(i),i,)

 

for i,v in enumerate(l):

    print(i,v)

1.12.3 eval:

res=eval('[1,2,3]')

print(res,type(res))

 

res=exec('[1,2,3]')

print(res)

1.12.4 pow

res=pow(2,3,3) # (2 ** 3 )%3

print(res)

1.12.5 round

print(round(3.5))